Spacy Ner

Spacy Ner

Opens a Streamlit app whose title points to named entity recognition with spaCy, for students and developers who want to see names picked out of text. The listing gives few other details.

Made by kingfisher on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: This is a small Streamlit app from kingfisher with a single main file. Its title points to named entity recognition with spaCy, which means picking out names of people, places and organizations from text. The source gives no other details and names no model. You use it in a browser, or you run a copy on your own computer.

Who it suits: It suits students and developers who want to see entity recognition on a sentence they write, and read how a short Streamlit app is built.

Industry | Who uses it | How they use it | Example
Education | Language processing students | See what an entity tagger picks out of a sentence | Open the Space and type a short sentence
Software | Python developers | Check a tagger on their own text before using it | Run a copy locally and read app.py
Research | Data analysts | Try a quick tagger on sample documents | Paste a few lines from a document

Skip it if you need to process thousands of files, because this is a small demo page.

Where to use it:

• A web browser: go to the Space on Hugging Face and try it with no install
• Windows, macOS or Linux: run a copy with Python

Use case | Who | What they get back
Tag a sentence | Students | The names the app finds in the text
Test on your text | Developers | A quick look at how the tagger behaves on their own writing
Read the code | Learners | A short app.py behind a Streamlit page

How to set it up: You need a few things in place first.

• Python with pip, on Windows, macOS or Linux
• An internet connection to fetch the files from Hugging Face

1. Install the Hugging Face hub package.
2. Download the Space into a local folder.
3. Move into that folder.
4. Install Streamlit and the packages listed in requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download kingfisher/spacy-ner --repo-type space --local-dir spacy-ner
cd spacy-ner
pip install streamlit
pip install -r requirements.txt
streamlit run app.py

How to use it: Online, use the Streamlit page the Space shows. On your own computer, start the app with the command below and open the page it serves in your browser.

streamlit run app.py

Example: You run streamlit run app.py inside the project folder, open the page and type a sentence that names a person and a city. The page shows the names the app found in it.

For newcomers and experts: If this is new to you, start online. Go to the Space and type one sentence with a name in it before you install anything. It shows what entity recognition means in practice.

If you know the field, note that the repository holds only a README, app.py and requirements.txt. The source names no model, label set or accuracy figure, so read app.py to see which spaCy pipeline it loads.

Open the Space or run streamlit
The Streamlit page loads
You type some text
The app tags the entities
The tagged text appears on the page

Good to know: The license tag is cc-by-nc-sa-4, which usually restricts commercial use, so read its terms before you reuse the code.

• License: cc-by-nc-sa-4
• Cost to run: none named, no API key, account or paid service
• Data it reaches: the text you type, with no outside account or web service listed

On Hugging Face, 13 November 2026

• Likes: 2
• Built with: streamlit
• License: cc-by-nc-sa-4.0

Links

• Space: https://huggingface.co/spaces/kingfisher/spacy-ner
• Files: https://huggingface.co/spaces/kingfisher/spacy-ner/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.